Wood Species Classification Research Articles

Performance of convolutional neural network (CNN) and performance influencing factors for wood species classification of Lepidobalanus growing in Korea

This study aimed to investigate the performance and factors affecting the species classification of convolutional neural network (CNN) architecture using whole-part and earlywood-part cross-sectional datasets of six Korean Quercus species. The accuracy of species classification for each condition was analyzed using the datasets, data augmentation, and optimizers—stochastic gradient descent (SGD), adaptive moment estimation (Adam), and root mean square propagation (RMSProp)—based on a CNN architecture with three to four convolutional layers. The model trained with the augmented dataset yielded significantly superior results in terms of classification accuracy compared to the model trained with the non-augmented dataset. The augmented dataset was the only factor affecting classification accuracy in the final five epochs. In contrast, four factors in the entire epoch, such as the Adam and SGD optimizers and the earlywood-part and whole-part datasets, affected species classification. The arrangement of earlywood vessels, broad rays, and axial parenchyma was identified as a major influential factor in the CNN species classification using gradient-weighted class activation mapping (Grad-CAM) analysis. The augmented whole-part dataset with the Adam optimizer achieved the highest classification accuracy of 85.7% during the final five epochs of the test phase.

Effect of wood surface finish on wood species classification using spectral reflectance

Wood species can be classified by spectral reflectance. It is unclear whether finish coated on the wood surface affects the accuracy of wood species classification. This paper focused on this issue, using the spectral reflectance of 8 different kinds of finish for wood species classification. The spectral reflectance of wood surface coated with finish was modified by the transfer model in order to reduce the effect of finish on classification accuracy. The experimental results show that it is not feasible to use the spectral reflectance of wood samples coated with finish directly to classify wood species; the best classification accuracy using the eight finishes was 30%. After correcting the spectrum of wood samples coated with finishes with the direct standardization (DS) transfer model, the classification accuracy of the near-infrared (NIR) spectrum was close to that of the original spectrum without finish. However, the visible/near-infrared (VIS/NIR) spectrum did not achieve a good classification effect after correction with the DS transfer model.

A Multi-Scale Convolutional Neural Network Combined with a Portable Near-Infrared Spectrometer for the Rapid, Non-Destructive Identification of Wood Species

The swift and non-destructive classification of wood species holds crucial significance for the utilization and trade of wood resources. Portable near-infrared (NIR) spectrometers have the potential for rapid and non-destructive wood species identification, and while several studies have explored related methodologies, further research on their practical application is needed. To address this research gap, this study proposes a multi-scale convolutional neural network (CNN) combined with a portable NIR spectrometer (wavelengths range: 908 to 1676 nm) for wood species identification. To enhance the capability of directly extracting robust features from NIR spectral data collected by a portable spectrometer, the Gramian angular field (GAF) method is introduced to transform 1-dimensional (1D) NIR spectral data into 2-dimensional (2D) data matrices. Furthermore, a multi-scale CNN model is utilized for direct feature extraction. The representation by 2D matrices, instead of 1D NIR spectral data, aligns with 2D convolutional operations and enables a more robust extraction of discriminative features. In the experimental phase, eight wood species were identified using the proposed method, alongside commonly used multivariate data analysis and machine learning (ML) methods. The StratifiedGroupKFold dataset partitioning approach and five-fold cross-validation were used. Additionally, nine spectral preprocessing methods were compared, and principal component analysis (PCA) was used for feature extraction in the ML method. Evaluation metrics, such as accuracy, precision, and recall, were adopted to assess the performance of the methods. The proposed multi-scale CNN model, in combination with 2D GAF matrices of the 1D spectral data, yielded the most accurate results with a mean accuracy of 97.34% in the five-fold validation. These findings present a new approach for the construction of a rapid, non-destructive, and automatic wood species identification method using a portable NIR spectrometer.

Klasifikacija vrsta drva prema slikama uz pomoć dvodimenzionalne konvolucijske neuronske mreže

The woodworking industry’s recognition and classification of timber is essential for trade, production and timber science. Traditional methods of identifying wood types are complex, time-consuming, costly and require expertise in wood science. Traditional techniques have been replaced by convolutional neural networks (CNNs), a deep learning tool to better identify wood species. In contrast to earlier studies that used pretrained models, a novel architecture designed explicitly for the WOOD-AUTH dataset was proposed in this study to develop a new 2D CNN model. The data collection encompasses high-level visual representations of 12 distinct types of timber. It is aimed to create a simpler and faster model as an alternative to time-consuming and heavy wood classification models. Compared to previous studies, this research worked with a newly structured 2D CNN network based on 12 wood species. High accuracy and fast computation time were achieved using fewer numbers (three layers) of the convolutional neural network. The proposed model achieved 94 % accuracy, 87 % precision, 81 % recall, 80 % F1 score and 112 minutes 27 seconds computation time. The 2D CNN model performed better than the transfer learning models regarding training epochs. The primary benefit of the model is its ability to achieve high accuracy with lower computation time, even at high epochs compared to other models. The introduced 2D CNN model produced satisfactory outcomes for wood species classification.

Fit-free analysis of fluorescence lifetime imaging data using chemometrics approach for rapid and nondestructive wood species classification

Abstract Conventional fluorescence spectroscopy has been suggested as a valuable tool for classifying wood species rapidly and non-destructively. However, because it is challenging to conduct absolute emission intensity measurements, fluorescence analysis statistics are difficult to obtain. In this study, another dimension of fluorescence, that is, fluorescence lifetime, was further evaluated to address this issue. A time-resolved fluorescence spectroscopic measurement system was first designed, mainly using a streak camera, picosecond pulsed laser at 403 nm, and a spectroscope, to collect the fluorescence time-delay (FTD) profiles and steady-state fluorescence intensity (FI) spectra simultaneously from 15 wood species. For data analysis, principal component analysis was used to “compress” the mean-centered FTD and FI spectra. Then, support vector machine classification analysis was utilized to train the wood species classification model based on their principal component scores. To avoid overfitting, ten-fold cross-validation was used to train the calibration model using 70 % of the total samples, and the remaining 30 % hold-out validation was used to test its reproducibility. The cross-validation accuracies were 100 % (5 softwoods) and 96 % (10 hardwoods), with test-validation accuracies of 96 % and 89 %.

Supervised classification of wood species during milling based on extracted cut events from ultrasonic air-borne acoustic signals

ABSTRACT Novel monitoring techniques are a prerequisite for adaptive manufacturing processes, which are of increasing importance to allow an efficient processing of heterogeneous raw materials such as wood. To establish an analysis framework for monitoring wood milling, an optical microphone with a sensitivity range from 10 Hz to 1 MHz was used. The gathered sound signals were segmented into individual cuts and transformed to the frequency domain by using the Fast Fourier Transformation. The spectral data were then labelled according to wood species and analysed with Principle Component Analysis. Finally, it was possible to obtain a Machine Learning classification model based on Linear Discriminant Analysis to distinguish between wood species with an average accuracy of 93.1% in validation and 91.9% in testing.

Shifted Excitation Raman Difference Spectroscopy Combined with Wide Area Illumination and Sample Rotation for Wood Species Classification.

Raman spectroscopy has found its way into a wide range of applications and is successfully applied for qualitative and quantitative studies. Despite significant technical progress over the last few decades, there are still some challenges that limit its more widespread usage. This paper presents a holistic approach to addressing simultaneously the problems of fluorescence interference, sample heterogeneity, and laser-induced sample heating. Long wavelength shifted excitation Raman difference spectroscopy (SERDS) at 830 nm excitation combined with wide-area illumination and sample rotation is presented as a suitable approach for the investigation of selected wood species. Wood as a natural specimen represents a well-suited model system for our study as it is fluorescent, heterogeneous, and susceptible to laser-induced modifications. Two different subacquisition times (50 and 100 ms) and two sample rotation speeds (12 and 60 r/min) were exemplarily assessed. Results demonstrate that SERDS can effectively separate the Raman spectroscopic fingerprints of the wood species balsa, beech, birch, hickory, and pine from intense fluorescence interference. Sample rotation in conjunction with 1 mm-diameter wide-area illumination was suitable to obtain representative SERDS spectra of the wood species within 4.6 s. Using partial least squares discriminant analysis, a classification accuracy of 99.4% for the five investigated wood species was realized. This study highlights the large potential of SERDS combined with wide-area illumination and sample rotation for the effective analysis of fluorescent, heterogeneous, and thermally sensitive specimens in a wide range of application areas.

Classification of Amazonian fast-growing tree species and wood chemical determination by FTIR and multivariate analysis (PLS-DA, PLS)

Fast-growing trees like Capirona, Bolaina, and Pashaco have the potential to reduce forest degradation because of their ecological features, the economic importance in the Amazon Forest, and an industry based on wood-polymer composites. Therefore, a practical method to discriminate specie (to avoid illegal logging) and determine chemical composition (tree breeding programs) is needed. This study aimed to validate a model for the classification of wood species and a universal model for the rapid determination of cellulose, hemicellulose, and lignin using FTIR spectroscopy coupled with chemometrics. Our results showed that PLS-DA models for the classification of wood species (0.84 ≤ R2 ≤ 0.91, 0.12 ≤ RMSEP ≤ 0.20, accuracy, specificity, and sensibility between 95.2 and 100%) were satisfied with the full spectra and the differentiation among these species based on IR peaks related to cellulose, lignin, and hemicellulose. Besides, the full spectra helped build a three-species universal PLS model to quantify the principal wood chemical components. Lignin (RPD = 2.27, {R}_{c}^{2} = 0.84) and hemicellulose (RPD = 2.46, {R}_{c}^{2} = 0.83) models showed a good prediction, while cellulose model (RPD = 3.43, {R}_{c}^{2} = 0.91) classified as efficient. This study showed that FTIR-ATR, together with chemometrics, is a reliable method to discriminate wood species and to determine the wood chemical composition in juvenile trees of Pashaco, Capirona, and Bolaina.

Micro image classification of 19 high-value hardwood species based on texture feature fusion

For classification of wood species with similar microstructure, 19 high-value hardwood species of Papilionaceae, Ebenaceae, and Caesalpiniaceae were used as experimental objects. Images of transverse sections, radial sections, and tangential sections were collected by Micro CT. Local binary patterns (LBP) are often used for feature extraction. LBP deformed forms such as uniform LBP, rotation-invariant LBP, and rotation-invariant uniform LBP were fused with Gray-Level Co-Occurrence Matrix (GLCM) to form three fusion features. The fusion features were combined with support vector machine (SVM) or BP neural network to realize wood classification. The texture feature fusion method was found to be better than the single feature classification. Among them, the effect of uniform LBP and GLCM fusion was the best, and the classification accuracy combined with SVM was the highest. The evaluation of the classification of 19 kinds of hardwood mainly depended on transverse sections, and its accuracy was higher than that of the radial and tangential sections. Therefore, the classification results of transverse sections should be taken as the main evaluation basis for the classification of the 19 high-value hardwood species.

Evaluation of Physical and Mechanical Properties of Selected Wood Species obtained from Saw Mills in Akure, Nigeria

The strength and sorption properties of selected wood species obtained from saw mills in Akure Nigeria were investigated based on key index properties such as density, bending strength and dimensional stability using appropriate standard experimental techniques. Data obtained from the experimental investigation revealed that the wood species ranged from low density 442.8 kg/m3 for Terminalia superba (Afara), through medium density 464 kg/m3 and 552.8 kg/m3 for Pycnanthus angolensis (Akomu) and Gmelina arborea (Gmelina) respectively, to high density 635.7kg/m3 and 678.2 kg/m3 for Milicia excelsa (Iroko) and Stereospermum acuminatissimum (Omo cedar) respectively. The bending strength values obtained qualified the wood species classification into low bending strength (25 N/mm2 – 75 N/mm2 ) for Afara, Akomu and Gmelina and the medium static bending strength in the range (75 N/mm2 – 125 N/mm2) for Iroko and Omo cedar. Furthermore, results of volumetric shrinkage and swelling are 7.21%: 5.04%, 7.75%: 5.26% and 7.84%: 6.69% for Afara, Akomu and Gmelina respectively while Iroko and Omo cedar recorded 10.95%: 7.84% and 11.08%: 11.08%. These updated results could be useful for wood selection for building construction application; for structural members in roofing, posts and beams.

An effective and fast solution for classification of wood species: A deep transfer learning approach

Classification and recognition of wood species have critical importance in wood trade, industry, and science. Therefore, accurate identification of wood species is a great necessity. Conventional classification and recognition of wood species require knowledge and experience on the anatomy of wood which is time-consuming, cost-ineffective, and destructive. Hence, convolutional neural networks (CNNs) -a deep learning tool- have replaced the conventional methods. In this study, classification of wood species via the WOOD-AUTH dataset and evaluating the performance of various deep learning architectures including ResNet-50, Inception V3, Xception, and VGG19 in classification with transfer learning was investigated in detail. The dataset contains macroscopic images of 12 wood species with three different types of wood sections: cross, radial and tangential. The experimental findings demonstrate that Xception produced a remarkable performance as compared to the other models in this study and the WOOD-AUTH dataset owners, yielding a classification accuracy of 95.88%.

Wood Species Recognition Based on Visible and Near-Infrared Spectral Analysis Using Fuzzy Reasoning and Decision-Level Fusion

A novel wood species spectral classification scheme is proposed based on a fuzzy rule classifier. The visible/near-infrared (VIS/NIR) spectral reflectance curve of a wood sample’s cross section was captured using a USB 2000-VIS-NIR spectrometer and a FLAME-NIR spectrometer. First, the wood spectral curve—with spectral bands of 376.64–779.84 nm and 950–1650 nm—was processed using the principal component analysis (PCA) dimension reduction algorithm. The wood spectral data were divided into two datasets, namely, training and testing sets. The training set was used to generate the membership functions and the initial fuzzy rule set, with the fuzzy rule being adjusted to supplement and refine the classification rules to form a perfect fuzzy rule set. Second, a fuzzy classifier was applied to the VIS and NIR bands. An improved decision-level fusion scheme based on the Dempster–Shafer (D-S) evidential theory was proposed to further improve the accuracy of wood species recognition. The test results using the testing set indicated that the overall recognition accuracy (ORA) of our scheme reached 94.76% for 50 wood species, which is superior to that of conventional classification algorithms and recent state-of-the-art wood species classification schemes. This method can rapidly achieve good recognition results, especially using small datasets, owing to its low computational time and space complexity.

Classification of wood species using spectral and texture features of transverse section

Classification of wood species using spectral and texture features of transverse section

Amazon wood species classification: a comparison between deep learning and pre-designed features

In many countries, the wood industry is a crucial sector and has a significant economic impact. In this sense, illegal logging is a way to reduce costs, avoiding taxes, or having access to more valuable wood species. To combat the latter, the recognition of wood species is crucial. However, this task is usually performed by experts through visual inspection, a process that requires sanding and cleaning the wood surface, and an impractical task for use in the field. In this paper, the acquisition process was simplified and a new wood dataset was introduced, where a simple pocket knife cut is used to expose the timber section for inspection. Four deep learning models with transfer learning were investigated and compared with traditional pre-designed feature methods. Additionally, the models were evaluated with a cross-validation scheme to avoid any bias. The experimental results show that deep learning outperforms pre-design features for wood classification. DenseNet achieved 98.13% of accuracy, indicating that it could be applied to assist untrained agents in wood classification.

Wood species recognition through FGLAM textural and spectral feature fusion

In this study, a method based on fuzzy gray level aura matrix (FGLAM) textural feature and spectral feature fusion is proposed to improve the accuracy of wood species classification. The experimental dataset is acquired by two sensors. The visible images of wood samples are acquired using a RGB image acquisition platform, and the spectral curves are acquired using a USB2000-VIS-NIR spectrometer having a spectral band of 350–1100 nm. Firstly, the textural features are computed by using FGLAM for gray level wood sample images. The original spectral curve is smoothed and corrected to remove noise. Secondly, the obtained textural features and spectral features are processed in extreme learning machine to complete the decision-level fusion and obtain the classification results. The experimental results for 15 wood species show that the classification accuracy of the textural feature by using FGLAM alone can reach to 90%, which is better than that acquired using the traditional gray level co-occurrence matrix, local binary pattern, and the mainstream improved basic gray level aura matrix. Moreover, the classification accuracy based on the FGLAM textural feature and spectral feature fusion proposed in this paper can reach to 100%. Finally, for 8 similar wood species, the recognition accuracy of the present fusion scheme reaches to 98.75%, which is also better than those state-of-the-art methods.

Classification of Wood Species Frequently Used for Modern and Ancient Buildings Utilizing Near-Infrared Spectroscopy with Multivariate Analysis and Enhancement of Its Generalization Performance

Classification of Wood Species Frequently Used for Modern and Ancient Buildings Utilizing Near-Infrared Spectroscopy with Multivariate Analysis and Enhancement of Its Generalization Performance

An analysis of timber sections and deep learning for wood species classification

The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential. We investigated the use of transfer learning from pre-trained deep neural networks for wood species classification and compared their results with a state-of-art pre-designed feature method. The experimental results show that traverse section images using a densely connected network achieved 98.7% of correct classification against 85.9% of standard pre-designed features.

Wood Species Identification using Convolutional Neural Network (CNN) Architectures on Macroscopic Images

Indonesia is a country that is very rich in tree species that grow in forests. Wood growth in Indonesia consists of around 4000 species that have different names and characteristics. These differences can determine the quality and exact use of each type of wood. The procedure of standard identification is currently still carried out through visual observation by the wood anatomist. The wood identification process is very in need of the availability of wood anatomists, with a limited amount of wood anatomist will affect the result and the length of time to make an identification. This thesis uses an identification system that can classify wood based on species names with a macroscopic image of wood and the implementation of the Convolutional Neural Network (CNN) method as a classification algorithm. Supporting architecture used is AlexNet, ResNet, and GoogLeNet. Architecture is then compared to a simple CNN architecture that is made namely Kayu30Net. Kayu30Net architecture has a precision performance value reaching 84.6%, recall 83.9%, F1 score 83.1% and an accuracy of 71.6%. In the wood species classification system using CNN, it is obtained that AlexNet as the best architecture that refers to a precision value of 98.4%, recall 98.4%, F1 score 98.3% and an accuracy of 96.7%.

An Incremental Self-Adaptive Wood Species Classification Prototype System

The present wood species classification systems can usually process the limited wood species quantity. We propose a novel incremental self-adaptive wood species classification system to solve the above-mentioned issue. The visible/near-infrared (VIS/NIR) spectrometer is used to pick up the spectral curves of wood samples for the subsequent wood species classification. First, when new wood samples of unknown wood species are added, they are classified as an unknown category by our one-class classifier, Support Vector Data Description (SVDD), while the existent wood species are classified as a known category by the SVDD. Second, the wood samples of known species are sent into the BP neural network for subsequent wood species classification. Third, the new wood samples of unknown species are sent into the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm for the unsupervised clustering, and the clustering result is evaluated by the internal and external norms. Last, if one cluster of one unknown species has an adequate amount of wood samples, these wood samples are removed and identified by human experts or other schemes to ensure to get the correct wood species name. Then, these wood samples are considered as a new known species and are sent into the classifiers, SVDD and BP neural network, to train them again. Experiments on 13 wood species prove the effectiveness of our prototype system with an overall classification accuracy of above 95%.

Effect of Organic Solvent Extractives on Korean Softwoods Classification Using Near-infrared Spectroscopy

This study analyzed the effect of organic solvent extractives on the classification of wood species via near-infrared spectroscopy (NIR). In our previous research, five species of Korean softwood were classified into three groups (i.e., Cryptomeria japonica (cedar)/Chamaecyparis obtuse (cypress), Pinus densiflora (red pine)/Pinus koraiensis (Korean pine), and Larix kaempferi (Larch)) using an NIR-based principal component analysis method. Similar tendencies of extractive distribution were observed among the three groups in that study. Therefore, in this study, we qualitatively analyzed extractives extracted by an organic solvent and analyzed the NIR spectra in terms of the extractives’ chemical structure and band assignment to determine their effect in more detail. Cedar/cypress showed a similar NIR spectra patterns by removing the extractives at 1695, 1724, and 2291 nm. D-pinitol, which was detected in cedar, contributed to that wavelength. Red pine/Korean pine showed spectra changes at 1616, 1695, 1681, 1705, 1724, 1731, 1765, 1780, and 2300 nm. Diterpenoids and fatty acid, which have a carboxylic group and an aliphatic double bond, contributed to that wavelength. Larch showed a catechin peak in gas chromatography and mass spectroscopy analysis, but it exhibited very small NIR spectra changes. The aromatic bond in larch seemed to have low sensitivity because of the 1st overtone of the O-H bond of the sawdust cellulose. The three groups sorted via NIR spectroscopy in the previous research showed quite different compositions of extractives, in accordance with the NIR band assignment. Thus, organic solvent extractives are expected to affect the classification of wood species using NIR spectroscopy.